3 papers
cs.LG2025
Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
Shiyuan Zuo, Xingrun Yan, Rongfei Fan +4
Federated Learning (FL) enables multiple clients to collaboratively train models without sharing raw data, but is vulnerable to Byzantine attacks and data heterogeneity, which can…
cs.LG2025
Consistent Estimation of Numerical Distributions under Local Differential Privacy by Wavelet Expansion
Puning Zhao, Zhikun Zhang, Bo Sun +4
Distribution estimation under local differential privacy (LDP) is a fundamental and challenging task. Significant progresses have been made on categorical data. However, due to dif…
cs.LG2025
On Theoretical Limits of Learning with Label Differential Privacy
Puning Zhao, Chuan Ma, Li Shen +2
Label differential privacy (DP) is designed for learning problems involving private labels and public features. While various methods have been proposed for learning under label DP…